5 citations · 16 across the 8 of their papers we have counts for
5 papers · 1 filter
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution
Tatiana A. Bubba, Luca Calatroni, Ambra Catozzi +6
Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven…
Automatic parameter selection for the TGV regularizer in image restoration under Poisson noise
Daniela di Serafino, Monica Pragliola
We address the image restoration problem under Poisson noise corruption. The Kullback-Leibler divergence, which is typically adopted in the variational framework as data fidelity t…
ADMM-based residual whiteness principle for automatic parameter selection in super-resolution problems
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
We propose an automatic parameter selection strategy for the problem of image super-resolution for images corrupted by blur and additive white Gaussian noise with unknown standard…
On and beyond Total Variation regularisation in imaging: the role of space variance
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
Over the last 30 years a plethora of variational regularisation models for image reconstruction has been proposed and thoroughly inspected by the applied mathematics community. Amo…
Residual whiteness principle for automatic parameter selection in - image super-resolution problems
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
We propose an automatic parameter selection strategy for variational image super-resolution of blurred and down-sampled images corrupted by additive white Gaussian noise (AWGN) wit…